Diabetes Prediction Model API

This is a FastAPI-based application that predicts whether a person is diabetic based on several health parameters using a pre-trained machine learning model.

Features

• Prediction Endpoint: Accepts multiple health-related parameters via query parameters and returns a prediction (diabetic or not diabetic).
• The model is pre-trained and saved using scikit-learn.

How It Works

The API uses a machine learning model trained to predict whether a person is diabetic or not based on the following input parameters:

• pregnancies (int): Number of pregnancies
• Glucose (int): Glucose level
• BloodPressure (int): Blood pressure level (mm Hg)
• SkinThickness (int): Triceps skin fold thickness (mm)
• Insulin (int): 2-Hour serum insulin (mu U/ml)
• BMI (float): Body Mass Index (weight in kg / (height in m²))
• DiabetesPedigreeFunction (float): Diabetes pedigree function
• Age (int): Age of the person

The API responds with a JSON object that contains the result:

{
  "result": "The person is diabetic"  // or "The person is not diabetic"
}

API Endpoint

GET /api/diabetes_prediction

You can make requests to the following URL:

https://diabetes-prediction-model-5d9550a8d37f.herokuapp.com/docs

Required Query Parameters:

• pregnancies: Integer
• Glucose: Integer
• BloodPressure: Integer
• SkinThickness: Integer
• Insulin: Integer
• BMI: Float
• DiabetesPedigreeFunction: Float
• Age: Integer

Example Request

Alternatively, instead of Swagger UI, you can directly enter the following URL into your browser with the parameters you want:

https://diabetes-prediction-model-5d9550a8d37f.herokuapp.com/api/diabetes_prediction?pregnancies=2&Glucose=120&BloodPressure=80&SkinThickness=20&Insulin=85&BMI=25.5&DiabetesPedigreeFunction=0.5&Age=33

Example Response

{
  "result": "The person is not diabetic"
}

Requirements

The project uses the following dependencies:

• FastAPI
• Uvicorn
• scikit-learn
• Pydantic
• Requests

Ensure that you install all dependencies listed in requirements.txt before running the project.

Running Locally

To run the project locally:

1. Clone the repository:

   git clone https://github.com/dev-achintha/-test-ML_Model_Deployement_using_Fast_API.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run the FastAPI application:

   uvicorn main:app --reload

4. Visit http://127.0.0.1:8000/docs in your browser to interact with the API using the FastAPI Swagger UI.